ADMINISTRATION CORE ABSTRACT The Pediatric Centers of Excellence in Nephrology funded by the NIDDK provide a focus to increase efficiency and promote collaborative efforts among groups of successful investigators at institutions with established comprehensive kidney research bases. We propose an administrative structure for the Children?s Hospital of Philadelphia (CHOP) Pediatric Center of Excellence in Nephrology that will form an interdisciplinary partnership between colleagues with expertise in design and analysis of observational studies and clinical trials, with individuals successful in initiating pediatric clinical trials within learning health systems, and colleagues with proven track records in research methods, clinical phenotyping and research training. The CHOP-PCEN will have three biomedical research cores in addition to the Administrative core; a Design and Analysis core, a Clinical phenotyping core focused on Nutrition/CVD risk factors and Bone health, and a Learning Health System Core, as well as a pilot and feasibility program, an enrichment core and two research project proposals utilizing the cores. The overall goal of the administrative core is to provide direction, guidance and oversight for the entire PCEN and assure its effective and efficient functioning. The Administrative core will interact with and engage our research base which includes 39 investigators, with 150 funded projects totaling over $35 million in annual direct costs. Over $12 million of this funding is directly relevant to pediatric nephrology led by likely users of the core services.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Specialized Center (P50)
Project #
1P50DK114786-01
Application #
9380703
Study Section
Special Emphasis Panel (ZDK1)
Project Start
Project End
Budget Start
2017-07-01
Budget End
2018-06-30
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Children's Hospital of Philadelphia
Department
Type
DUNS #
073757627
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
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Zheng, Qiang; Warner, Steven; Tasian, Gregory et al. (2018) A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images. Acad Radiol 25:1136-1145
Li, Hongming; Galperin-Aizenberg, Maya; Pryma, Daniel et al. (2018) Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiother Oncol 129:218-226
Li, Hongming; Zhu, Xiaofeng; Fan, Yong (2018) Identification of Multi-scale Hierarchical Brain Functional Networks Using Deep Matrix Factorization. Med Image Comput Comput Assist Interv 11072:223-231
Li, Hongming; Fan, Yong (2018) Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks. Med Image Comput Comput Assist Interv 11072:320-328
Li, Hongming; Fan, Yong (2018) Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI. Med Image Comput Comput Assist Interv 11072:232-239
Li, Hongming; Fan, Yong (2018) NON-RIGID IMAGE REGISTRATION USING SELF-SUPERVISED FULLY CONVOLUTIONAL NETWORKS WITHOUT TRAINING DATA. Proc IEEE Int Symp Biomed Imaging 2018:1075-1078
Li, Hongming; Satterthwaite, Theodore D; Fan, Yong (2018) BRAIN AGE PREDICTION BASED ON RESTING-STATE FUNCTIONAL CONNECTIVITY PATTERNS USING CONVOLUTIONAL NEURAL NETWORKS. Proc IEEE Int Symp Biomed Imaging 2018:101-104
Zhu, Xiaofeng; Zhang, Weihong; Fan, Yong et al. (2018) A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis. Neuroinformatics 16:351-361